ICJIA R Workshop

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Source: Time Magazine

# peak at the first rows of the data
head(ispcrime_tbl)

# A tibble: 6 x 12
   year county viol~ murd~  rape robb~ aggA~ prop~ burg~ larc~ MVTft arson
  <int> <fctr> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
1  2011 Adams    218     0    37    15   166  1555   272  1241    36     6
2  2011 Alexa~   119     0    14     4   101   290    92   183    11     4
3  2011 Bond       6     1     0     0     5   211    58   147     5     1
4  2011 Boone     59     0    24     8    27   733   152   563    14     4
5  2011 Brown      7     0     1     0     6    38    14    22     1     1
6  2011 Bureau    42     0     4     3    35   505    90   405     8     2

# get a quick summary of each column
summary(ispcrime_tbl)

      year            county     violentCrime       murder       
 Min.   :2011   Adams    :  5   Min.   :    0   Min.   :  0.000  
 1st Qu.:2012   Alexander:  5   1st Qu.:   19   1st Qu.:  0.000  
 Median :2013   Bond     :  5   Median :   42   Median :  0.000  
 Mean   :2013   Boone    :  5   Mean   :  501   Mean   :  7.026  
 3rd Qu.:2014   Brown    :  5   3rd Qu.:  133   3rd Qu.:  1.000  
 Max.   :2015   Bureau   :  5   Max.   :33348   Max.   :566.000  
                (Other)  :480   NA's   :7       NA's   :7        
      rape            robbery          aggAssult       propertyCrime   
 Min.   :   0.00   Min.   :    0.0   Min.   :    0.0   Min.   :     0  
 1st Qu.:   1.00   1st Qu.:    0.0   1st Qu.:   15.0   1st Qu.:   133  
 Median :   6.00   Median :    2.0   Median :   33.0   Median :   349  
 Mean   :  41.29   Mean   :  172.3   Mean   :  280.4   Mean   :  2913  
 3rd Qu.:  22.00   3rd Qu.:   13.0   3rd Qu.:  102.0   3rd Qu.:  1190  
 Max.   :1986.00   Max.   :16095.0   Max.   :15129.0   Max.   :178902  
 NA's   :7         NA's   :7         NA's   :7         NA's   :7       
    burglary         larcenyTft           MVTft             arson        
 Min.   :    0.0   Min.   :     0.0   Min.   :    0.0   Min.   :   0.00  
 1st Qu.:   35.5   1st Qu.:    85.5   1st Qu.:    3.0   1st Qu.:   1.00  
 Median :   79.0   Median :   258.0   Median :   10.0   Median :   2.00  
 Mean   :  589.3   Mean   :  2084.9   Mean   :  215.2   Mean   :  23.45  
 3rd Qu.:  268.0   3rd Qu.:   852.0   3rd Qu.:   30.0   3rd Qu.:   8.50  
 Max.   :38485.0   Max.   :116145.0   Max.   :22879.0   Max.   :1418.00  
 NA's   :7         NA's   :7          NA's   :7         NA's   :7        

# filter to keep only counties starting with C for 2015
#   while creating and showing a new variable for total crime count
ispcrime_tbl %>%
  filter(substr(county, 1, 1) == "C", year == 2015) %>%
  mutate(totalCrime = violentCrime + propertyCrime) %>%
  select(year, county, totalCrime)

# A tibble: 12 x 3
    year county     totalCrime
   <int> <fctr>          <int>
 1  2015 Calhoun            NA
 2  2015 Carroll           176
 3  2015 Cass              154
 4  2015 Champaign        6486
 5  2015 Christian         292
 6  2015 Clark             103
 7  2015 Clay              191
 8  2015 Clinton           423
 9  2015 Coles             805
10  2015 Cook           153575
11  2015 Crawford          282
12  2015 Cumberland         42

# get annual average count of violent crime by county
ispcrime_tbl %>%
  group_by(county) %>%
  summarise(annualAvgCrime = sum(violentCrime, propertyCrime, na.rm = TRUE) / 5)

# A tibble: 102 x 2
   county    annualAvgCrime
   <fctr>             <dbl>
 1 Adams             1724  
 2 Alexander          385  
 3 Bond               190  
 4 Boone              426  
 5 Brown               39.0
 6 Bureau             480  
 7 Calhoun             13.8
 8 Carroll            196  
 9 Cass               109  
10 Champaign         6567  
# ... with 92 more rows